The global pandemic of Covid-19 led to a crisis of not only fatal impact but financially. The stock market experienced one of its biggest market crashes ever and the young investors of our generation experienced something that they could never imagine. Their financial situation and investments suddenly changed and many of them did not know how to act or behave during this difficult period. This paper is based on qualitative research where ten different young investors have been interviewed to determine if they have matured and how their behavior has changed financially. The two research questions for the paper are: Is Covid-19 a blessing in disguise forcing young investors to mature, increasing their financial literacy, and thus change their investment behavior? and how has the Covid-19 pandemic affected young investors decision making on the stock market? The study and research questions were determined using three theories which are the efficient market hypothesis, behavioral finance, and the stages of change model. All three models were used to see how an individual behave during a financial crisis. The efficient market hypothesis basically argues that all relevant information matches the prices of stocks at any given time. Behavioral finance examines how an individual behave in a financial setting such as investment behaviors. Herding behavior and overconfidence are two cognitive biases within behavioral finance that was easily seen in the individuals for this study. The stages of change model states that an individual go through five different stages when a change of behavior is needed. This model could be applied on any individual, but a change of investments behavior should not go through so many stages as the model has so it had some limitations on the individuals in the study. The results and findings of the paper states that young investors have learned a valuable lesson from the Covid-19 pandemic increasing their financial literacy and creating more sustainable strategies for future investments.

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This study introduces a multi-objective optimization framework for the re-dimensioning of the cross-section elements of rural horizontal curves with limited sight distance. The optimization aims at minimizing both the risk of collision associated with the limited sight distance and the expected collision frequency corresponding to the cross-section elements’ dimensions. The risk component was assessed using an index known as (Pnc), which is developed based on the reliability theory. The change in collision frequency corresponding to the change of the cross-section elements was extracted from the literature. The risk and the safety components were then combined into one measure (CMFcombined) to develop a direct measure of the safety impacts of the optimization. The proposed framework was applied to five restricted curves in British Columbia, Canada, considering various scenarios. The results showed a considerable reduction in the Pnc value (ranging from 12% to 73%) and the expected collision frequency (ranging from 10% to 31%) after optimization. The estimated combined reduction in collision frequency (CMFcombined) was estimated to vary between 48% and 76%. The results showed that the optimization of cross-section elements can improve the safety of horizontal curves significantly. The framework presented in this study would support transportation engineers in selecting optimal dimensions of cross-section elements of restricted horizontal curves, understanding the safety consequences of selecting a specific cross-section configuration, and assessing the economic viability of different design options. / Thesis / Master of Applied Science (MASc)

Movements of cyclists and m.otor vehicles at intersections involve a wide variety of potential conflicting interactions. In Australia, the high numbers of motor vehicles, particularly light passenger vehicles, mixed with cyclists results in many bicycle-light passengervehicle (LPV) crashes (3,135 crashes during 2002-2014). About 68% of cyclist deaths at Australian intersections in 2016 were due to crashes between bicycles and LPVs (DITRLDG, 2016). The high number ofLPV crashes among fatalities among cyclists is an increasing safety concem. When an LPV collides with a cyclist, the resulting impact forces in.tluence the probability of cyclist injury severity outcom.e. Therefore, the goa1 at intersections should be to understand whether and which particular crash patterns are more injurious, in order to better inform approaches to reduce the impact forces to levels that do not result in severe injury outcomes. To examine how crash pattem (or mechanism) influences the injury severity of cyclists in bicycle-motor vehicle crashes at intersections, researchers typically describe the crash mechanism in terms of crash types, such as angle crashes, head--on crashes, rear-end crashes, and sideswipe crashes (e.g., Kim et al., 2007; Pai, 2011 ). While crash types explain crash mechanisms to some extent, this study hypothesiz.es that the trajectories of the crash involved vehicles may provide additional information because they better capture the movements of the vehicles prior to collision. Furthermore, it is argued that injury pattem might be in.tluenced by vehicle travel direction and manoeuvre (Isaksson-Hellman and Wemeke, 2017). For example, when a car is moving straight ahead it is likely to have a higher speed than when it is turning, and if cyclists are struck at a higher impact speed, they tend to sustain more severe injury (Badea-Romero and Lenard, 2013). While many studies have evaluated the association between cyclist injwy severity and crash types, the factors that might influence cyclist injury severity related to trajectory types (vehicle movement and travel direction) have not yet been thoroughly investigated. This study aims to examine the factors associated with cyclists' injury severity for 'trajectory types• compared with the typically used 'crash types' at intersections.

Road crashes pose a significant public health issue worldwide, often leading to severe injuries and fatalities. This dissertation embarks on a comprehensive examination of the factors affecting road crash severity, with a special focus on older drivers and the unique challenges introduced by the COVID-19 pandemic. Utilizing a dataset from Virginia, USA, the research integrates advanced statistical methods and machine learning techniques to dissect this critical issue from multiple angles. The initial study within the dissertation employs multilevel ordinal logistic regression to assess crash severity among older drivers, revealing the complex interplay of various factors such as crash type, road attributes, and driver behavior. It highlights the increased risk of severe crashes associated with head-on collisions, driver distraction or impairment, and the non-use of seat belts, specifically affecting older drivers. These findings are pivotal in understanding the unique vulnerabilities of this demographic on the road. Furthermore, the dissertation explores the efficacy of both parametric and non-parametric machine learning models in predicting crash severity. It emphasizes the innovative use of synthetic resampling techniques, particularly random over-sampling examples (ROSE) and synthetic minority over-sampling technique (SMOTE), to address class imbalances. This methodological advancement not only improves the accuracy of crash severity predictions for severe crashes but also offers a comprehensive understanding of diverse factors, including environmental and roadway characteristics. Additionally, the dissertation examines the influence of the COVID-19 pandemic on road safety, revealing a paradoxical decrease in overall traffic crashes accompanied by an increase in the rate of severe injuries. This finding underscores the pandemic's transformative effect on driving behaviors and patterns, heightening risks for vulnerable road users like pedestrians and cyclists. The study calls for adaptable road safety strategies responsive to global challenges and societal shifts. Collectively, the studies within this dissertation contribute substantially to transportation safety research. They demonstrate the complex nature of factors influencing crash severity and the efficacy of tailored approaches in addressing these challenges. The integration of advanced statistical methods with machine learning techniques offers a profound understanding of crash dynamics and sets a new benchmark for future research in transportation safety. This dissertation underscores the evolving challenges in road safety, especially amidst demographic shifts and global crises, and advocates for adaptive, evidence-based strategies to enhance road safety for all, particularly vulnerable groups like the older drivers. / Doctor of Philosophy / Road crashes are a major concern worldwide, often leading to serious injuries and loss of life. This dissertation delves into the critical issue of road crash severity, with a special focus on older drivers and the challenges brought about by the COVID-19 pandemic. Drawing on data from Virginia, USA, the research combines cutting-edge statistical methods and machine learning to shed light on this pressing matter. One important part of the research focuses on older drivers. It uses advanced analysis to find out why crashes involving this group might be more serious. The study discovered that situations like head-on collisions, driver distraction or impairment, and not wearing seat belts greatly increase the risk for older drivers. Understanding these risks is crucial in identifying the special needs of older drivers on the road. Then, the study explores the power of machine learning in predicting crash severity. Here, the research stands out by using innovative techniques to balance out the data, leading to more accurate predictions. This part of the study not only improves our understanding of what leads to severe crashes but also highlights how different environmental and road factors play a role. Following this, the research looks at how the COVID-19 pandemic has impacted road safety. Interestingly, while the overall number of crashes went down during the pandemic, the rate of severe injuries in the crashes that occurred increased. This suggests that the pandemic changed driving behaviors, posing increased risks especially to pedestrians and cyclists. In summary, this dissertation offers valuable insights into the complex factors affecting road crash severity. It underscores the importance of using advanced analysis techniques to understand these dynamics better, especially in the face of demographic changes and global challenges like the pandemic. The findings are not just academically significant; they provide practical guidance for policymakers and road safety experts to develop strategies that make roads safer for everyone, particularly older drivers.

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Thesis (M.Med. (Forensic Pathology)) -- University of Limpopo, 2022 / Introduction and background: Road traffic fatalities remain a worldwide burden with more than half of those fatalities comprising of vulnerable road users (pedestrians, cyclists and motorcyclists). This prompted the World Health Organization and United Nations to establish Sustainable Developmental Goals aimed at reducing road traffic crashes. The study explored factors relating to pedestrian fatalities on the R71 road, which may inform future interventions to enhance pedestrian safety. Aim: The study aimed at profiling pedestrian road traffic crash fatalities on the R71 road admitted at Polokwane Forensic Pathology Services. Methodology: A quantitative descriptive study utilising total population purposive sampling of pedestrians that demised because of R71 road traffic crashes over a 3-year period was done. There were 65 cases studied. Results: The study revealed that the fatalities were more male adult pedestrians than females, who were between 20 -39 years old. Majority of those pedestrians were wearing dark coloured clothing with no reflectors on. The pedestrian fatalities were mostly seen over the weekend and between evening and midnight. The fatalities peaked in December and February (summer season). The common locality of the pedestrian fatalities was Mankweng and Mentz village (Area 3). Most of those pedestrians sustained head injuries. Conclusion: Contributory factors and injuries of those pedestrian fatalities that demised because of R71 road traffic crashes were identified, which some were similar to those already highlighted in literature.

Quality software usually means high reliability, which in turn has two main components; the software should provide correctness, which means it should perform the specified task, and robustness in the sense that it should be able to manage unexpected situations. In other words, reliable systems are systems without bugs. Because of this, testing and debugging are recurrent and resource expensive tasks in software development, notably in large software systems. This thesis investigate the potential of using unsupervised machine learning on Ericsson bug reports to avoid unnecessary debugging by identifying duplicate bug reports. The bug report data that is considered are crash dumps from software crashes. The data is clustered using the clustering algorithms k-modes, k-prototypes and expectation maximization where-after the generated clusters are used to assign new incoming bug reports to the previously generated clusters, thus indicating whether an old bug report is similar to the newly submitted one. Due to the dataset only being partially labeled both internal and external validity indices are applied to evaluate the clustering. The results indicate that many, small clusters can be identified using the applied method. However, for the results to have high validity the methods could be applied on a larger data set. / Mjukvara av hög kvalitet innebär ofta hög tillförlitlighet, vilket i sin tur har två huvudkomponenter; mjukvaran bör vara korrekt, den ska alltså uppfylla dom specifierade kraven, och dessutom robust vilket innebär att den ska kunna hantera oväntade situationer. Med andra ord, tillförlitliga system är system utan buggar. På grund av detta är testning och felsökning återkommande och resurskrävande uppgifter inom mjukvaruutveckling, i synnerhet för stora mjukvarusystem. Detta arbete utforskar vilken potential oövervakad maskininlärning på Ericssons felrapporter har för att undvika onödig felsökning genom att identifiera felrapporter som är dubletter. Felrapporterna som används i detta arbete innehåller data som sparats i minnet vid en mjukvarukrasch. Data klustras sedan med klustringsalgoritmerna k-modes, k-prototypes och expectation maximization varpå dom genererade klustren används för att tilldela nya inkommande felrapporter till de tidigare generade klustren, för att på så sätt kunna identifiera om en gammal felrapport är lik en ny felrapport. Då de felrapporter som behandlas endast till viss del redan är märkta som dubletter används både externa och interna valideringsmått för att utvärdera klustringen. Resultaten tyder på att många, små kluster kunde identifieras med de använda metoderna. Dock skulle metoderna behöva appliceras på ett dataset med större antal felrapporter för att resultaten ska få hög validitet.

No / The widespread protests of November 2019 may be marked as the bloodiest recent chapter of the Islamic Republic of Iran's history in terms of popular dissent. The two major protests in December 2017 and November 2019, followed by the public reaction to the shooting down of the Ukrainian International Airlines Flight 752 by the IRGC over Tehran after the US killing of General Soleimani, suggest that the prevailing dynamics of political protest in Iran are changing. There is an increasing sense of radicalisation among protesters, while the state is prepared to resort to extreme violence to maintain control. The geography of political protest has changed. The declining economic situation has had a profound impact on the more vulnerable segments of the society who are now increasingly playing a more proactive role in challenging the state. The methods of protest have been evolving over the last four decades, especially in the cultural arena. Last but not least, the willingness of the protesters both to endure and inflict violence is precipitously transforming state-society relations beyond recognition. This article begins by providing a brief overview of protest in the history of the Islamic Republic, up to the public reaction to the 2020 downing of the Ukrainian airline over Tehran. This provides a historical context to assess the ways in which both the political climate and protests have changed over the last four decades. A section identifying and analysing the factors which have created the current political cul-de-sac then follows. The changing dynamics of the protests are the result of the existing political gridlock and the economic crisis, and it is thus important to evaluate the prevailing conditions which have paved the way for the radicalisation of political climate in Iran. The final section examines the changing dynamics of political protest.